Distribution systems are characterized in that various types of loads are mixed and load fluctuation is high. Furthermore, as time elapses, power losses increase for reasons, such as new installations and extensions in factories, buildings and homes and increases in the demand of power, thereby worsening imbalance between lines.
In order to overcome the above problem, the reconfiguration of a distribution system that is intended to change the structure of the distribution system using changes in the locations and states of linked and section switches in a system.
With regard to a representative technique, a distribution system is reconfigured using an optimality-guaranteed branch and bound method of calculating the losses of an open system while successively opening the switches of a distribution system in a state in which all the switches have been closed. Furthermore, with regard to another technique, a distribution system is reconfigured using a branch exchange method of changing the configuration of the distribution system by selecting a section switch and a linked switch and changing the states of the switches. In addition, a technique using a simulated annealing (SA) method, that is, approximate optimal solution search, and a technique using a tabu search method, that is, tabu search, are used.
Although the loads of a system should be accurately known in order to apply the techniques for the reconfiguration of a distribution system to sites in the field, the conventional techniques are problematic in that they cannot acquire an optimized solution in practice because they ignore the above-described point.
In order to overcome this problem, it is necessary to measure loads in real time, acquire an optimal solution in real time, and change the configuration of a system in real time, but this is impossible in practice. Even if it were possible, the load would have already changed to a pattern different from that at the time of the measurement, and thus there is a limitation in that an acquired solution is still not an optimal solution.
In addition, in order to measure an optimal solution in real time, it is necessary to replace equipment, infrastructure, and systems at sites in the field, which requires massive investment. Moreover, even though a solution is optimal in a current period, the solution is still not an optimal solution because the load frequently changes. Accordingly, only an effect in a limited range can be expected, and thus a problem arises in that it is difficult to considerably improve investment versus efficiency.